Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing
Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remai...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4431 |
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| author | Parisa Esmaili Luca Martiri Parvaneh Esmaili Loredana Cristaldi |
| author_facet | Parisa Esmaili Luca Martiri Parvaneh Esmaili Loredana Cristaldi |
| author_sort | Parisa Esmaili |
| collection | DOAJ |
| description | Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery. |
| format | Article |
| id | doaj-art-66b4e0ebc24d457db2bd45756aae78a7 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-66b4e0ebc24d457db2bd45756aae78a72025-08-20T03:32:18ZengMDPI AGSensors1424-82202025-07-012514443110.3390/s25144431Cycle-Informed Triaxial Sensor for Smart and Sustainable ManufacturingParisa Esmaili0Luca Martiri1Parvaneh Esmaili2Loredana Cristaldi3Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, I-20133 Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, I-20133 Milan, ItalyDepartment of Computer Engineering, Cyprus International University, Northern Cyprus, via Mersin 10, 99258 Nicosia, TürkiyeDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, I-20133 Milan, ItalyAdvances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery.https://www.mdpi.com/1424-8220/25/14/4431predictive maintenanceempirical mode decompositiontriaxial accelerometervibration analysisCNC machiningcondition monitoring |
| spellingShingle | Parisa Esmaili Luca Martiri Parvaneh Esmaili Loredana Cristaldi Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing Sensors predictive maintenance empirical mode decomposition triaxial accelerometer vibration analysis CNC machining condition monitoring |
| title | Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing |
| title_full | Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing |
| title_fullStr | Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing |
| title_full_unstemmed | Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing |
| title_short | Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing |
| title_sort | cycle informed triaxial sensor for smart and sustainable manufacturing |
| topic | predictive maintenance empirical mode decomposition triaxial accelerometer vibration analysis CNC machining condition monitoring |
| url | https://www.mdpi.com/1424-8220/25/14/4431 |
| work_keys_str_mv | AT parisaesmaili cycleinformedtriaxialsensorforsmartandsustainablemanufacturing AT lucamartiri cycleinformedtriaxialsensorforsmartandsustainablemanufacturing AT parvanehesmaili cycleinformedtriaxialsensorforsmartandsustainablemanufacturing AT loredanacristaldi cycleinformedtriaxialsensorforsmartandsustainablemanufacturing |